Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle
Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle
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DOI：
10.1080/01621459.2014.889022
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发表时间：
2014-07-03
影响因子：
3.7
通讯作者：
Zhong, Ming
中科院分区：
文献类型：
--
作者：
Aue, Alexander;Cheung, Rex C. Y.;Zhong, Ming
本文提出了新的模型拟合技术的观测数据序列的分位数，包括数据分割和变量选择的方法。然而，主要的贡献是提供了一种同时执行这两项任务的手段。这是通过将数据与最佳拟合分段分位数回归模型相匹配来实现的，其中拟合由来自最小描述长度原则的惩罚来确定。由此产生的优化问题的解决与使用遗传算法。所提出的，全自动的程序，不像传统的断点程序，不基于重复的假设检验，不需要，不像大多数变量选择程序，规范的调整参数。理论上的大样本性质。与现有的分位数断点和变量选择方法的实证比较表明，新的程序在实践中工作得很好。
This article proposes new model-fitting techniques for quantiles of an observed data sequence, including methods for data segmentation and variable selection. The main contribution, however, is in providing a means to perform these two tasks simultaneously. This is achieved by matching the data with the best-fitting piecewise quantile regression model, where the fit is determined by a penalization derived from the minimum description length principle. The resulting optimization problem is solved with the use of genetic algorithms. The proposed, fully automatic procedures are, unlike traditional break point procedures, not based on repeated hypothesis tests, and do not require, unlike most variable selection procedures, the specification of a tuning parameter. Theoretical large-sample properties are derived. Empirical comparisons with existing break point and variable selection methods for quantiles indicate that the new procedures work well in practice.